AIMC Topic: Humans

Clear Filters Showing 63041 to 63050 of 95995 articles

Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks.

Artificial intelligence in medicine
Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in clinical, which provides valuable information for understanding the physiological mechanisms behind...

Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

Medical image analysis
Deep learning networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets, particularly on metrics focused on large pathologies. For diseases such as Multiple Sclerosis (MS), however,...

An analysis of training and generalization errors in shallow and deep networks.

Neural networks : the official journal of the International Neural Network Society
This paper is motivated by an open problem around deep networks, namely, the apparent absence of over-fitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze this phenomenon in the case ...

Spiking Neural Networks and online learning: An overview and perspectives.

Neural networks : the official journal of the International Neural Network Society
Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they...

External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Preoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG ML 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease were pre...

Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning.

International journal of radiation oncology, biology, physics
PURPOSE: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) usin...

Clustering suicides: A data-driven, exploratory machine learning approach.

European psychiatry : the journal of the Association of European Psychiatrists
Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into "violent" versus "non-violent" method. Interestingly, since the proposition of this dichot...

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol.

BMC psychiatry
BACKGROUND: The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information's for patient management. Artificial intelligence (AI) techniques allow processing o...

Ten years of knowledge representation for health care (2009-2018): Topics, trends, and challenges.

Artificial intelligence in medicine
BACKGROUND: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representa...